Distributed Probabilistic Search and Tracking of Agile Mobile Ground Targets Using a Network of Unmanned Aerial Vehicles

As technologies in digital computation, sensing, wireless and wired communications, embedded systems, and micro-electro-mechanical systems continue to advance in the coming years, it is certain that we will see a variety of distributed sensor networks (DSNs) being deployed in an increasing number of systems such as power distribution systems, engineering structures and buildings, smart homes, environmental monitoring systems, biomedical systems, military systems, and others. In addition, unlike the traditional networks of sensors, the mobility afforded by autonomous systems, embedded systems, and humans who carry smart sensing devices will contribute in creating new and exciting future sensor networks. These future networks of sensors that take advantage of man-machine interactions will also introduce new applications yet unknown to us. In this paper, we present the origin and time line of DSN development, analyze the benefits and challenges of DSNs, and present a mobile sensor network in the form of an unmanned aerial vehicle (UAV) team using distributed mission area probability maps to search and track mobile ground targets. We propose a novel update strategy for the probability map used by UAVs to store probability information of dynamic target locations in the search area. Two update laws are developed to accommodate maps with different scales. Simulation results are used to demonstrate the validity of the proposed probability-map update strategy.

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